code: def build_sac_model_and_action_dist(
      policy: Policy,
      obs_space: gym.spaces.Space,
      action_space: gym.spaces.Space,
      config: AlgorithmConfigDict,
  ) -> Tuple[ModelV2, Type[TorchDistributionWrapper]]:
      """Constructs the necessary ModelV2 and action dist class for the Policy.
  
      Args:
          policy: The TFPolicy that will use the models.
          obs_space (gym.spaces.Space): The observation space.
          action_space (gym.spaces.Space): The action space.
          config: The SACConfig object.
  
      Returns:
          ModelV2: The ModelV2 to be used by the Policy. Note: An additional
              target model will be created in this function and assigned to
              `policy.target_model`.
      """
      model = build_sac_model(policy, obs_space, action_space, config)
      action_dist_class = _get_dist_class(policy, config, action_space)
      return model, action_dist_class
  
imports: import gymnasium
  from gymnasium.spaces import Box, Discrete
  import logging
  import tree
  from typing import Dict, List, Optional, Tuple, Type, Union
  import ray
  import ray.experimental.tf_utils
  from ray.rllib.algorithms.sac.sac_tf_policy import build_sac_model, postprocess_trajectory, validate_spaces
  from ray.rllib.algorithms.dqn.dqn_tf_policy import PRIO_WEIGHTS
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchCategorical, TorchDistributionWrapper, TorchDirichlet, TorchSquashedGaussian, TorchDiagGaussian, TorchBeta
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.policy_template import build_policy_class
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.spaces.simplex import Simplex
  from ray.rllib.policy.torch_mixins import TargetNetworkMixin
  from ray.rllib.utils.torch_utils import apply_grad_clipping, concat_multi_gpu_td_errors, huber_loss
  from ray.rllib.utils.typing import LocalOptimizer, ModelInputDict, TensorType, AlgorithmConfigDict
label_desc: Use image node_id 2 for calling a global function with example usage: build_sac_model_and_action_dist(policy, obs_space, action_space, config) and returns: model,  action_dist_class